Due to the heterogeneous quality-of-service requirement of users in NOMA, where users in the same cluster forming NOMA should have diversified channel gains and quality of service, user behaviour prediction is of great importance for the implementation of NOMA networks.
To predict the users behaviors such as mobility patterns, each user in FSL scheme performs a supervised learning algorithm to train the learning model using its own user behavior data and upload the trained model to the BS via NOMA. Then the BS generates and broadcasts the unified learning model parameters to all users by using NOMA. Based on the mobility patter predictions, the users can dynamically choose subchannel to upload data in the uplink, the BS dynamically allocates multiple subchannels to multiple users in the downlink, and multiple users which occupy the same subchannel can perform NOMA.
For multiple BSs to predict the quality of service of users [1]} in FSL, each BS uses supervised learning algorithm based on its stored information such as users' requested data, gender, job, and device type and all BSs transmit the learning model results to a server via NOMA to get a unified federated learning model.
